Reconstructing temperature at Egelsee,Switzerland, using North American and Swedish chironomid transfer functions: potential and pitfalls

The temperature reconstruction obtained from chironomids preserved in the sediment of Egelsee, Switzerland, was partially flawed by the low percentages of fossil taxa represented in the Swiss calibration set (Larocque-Tobler et al. 2009a). Transfer functions (TFs) from other regions, which allow a good representation of the fossil taxa (>80%), could be applied to the fossil assemblages of Egelsee. First, the validity of using two (a Swedish and a North American (NA)) TFs was tested by comparing the chironomid-inferred temperatures with instrumental data. Since good relationships (r _Pearson = 0.71 and 0.61, p = 0.001 for the NA and Swedish TFs, respectively) were obtained, these two models were used to reconstruct the Late Glacial and early Holocene periods at Egelsee. Reconstructions using both models showed clear cold periods during the Younger Dryas and the so-called 8,200 calibrated years BP event. However, the amplitude of changes during these periods was higher when the NA transfer function was used, probably due to the fact that 37% of the taxa in the core had temperature optima colder in the NA than in the Swedish and Swiss models. The results indicate that TFs from other regions can be applied when they are based on samples with good modern analogues, however, caution should be taken when the amplitude of temperature changes is considered.     

A digital procedure for ground water recharge and discharge pattern recognition and rate estimation

A digital procedure to estimate recharge/discharge rates that requires relatively short preparation time and uses readily available data was applied to a setting in central Wisconsin. The method requires only measurements of the water table, fluxes such as stream baseflows, bottom of the system, and hydraulic conductivity to delineate approximate recharge/discharge zones and to estimate rates. The method uses interpolation of the water table surface, recharge/discharge mapping, pattern recognition, and a parameter estimation model. The surface interpolator used is based on the theory of radial basis functions with thin-plate splines. The recharge/discharge mapping is based on a mass-balance calculation performed using MODFLOW. The results of the recharge/discharge mapping are critically dependent on the accuracy of the water table interpolation and the accuracy and number of water table measurements. The recharge pattern recognition is performed with the help of a graphical user interface (GUI) program based on several algorithms used in image processing. Pattern recognition is needed to identify the recharge/discharge zonations and zone the results of the mapping method. The parameter estimation program UCODE calculates the parameter values that provide a best fit between simulated heads and flows and calibration head-and-flow targets. A model of the Buena Vista Ground Water Basin in the Central Sand Plains of Wisconsin is used to demonstrate the procedure.     

A method of computing the gravitational field of an axially symmetric flat galaxy

A method has been developed for computing the gravitational force field of an axially symmetric flat galaxy from its surface mass density. The method is simple to program, fast, and accurate. An inversion formula is derived that allows computation of surface density from rotation curves by use of any method that converts density to force. The method is compared with a method of Clutton-Brock that utilizes Hankel transforms of Laguerre functions.     

Predicting conductance due to upconing using neural networks

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.     

A neural network model for predicting aquifer water level elevations

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.     

Airborne detection of ecosystem responses to an extreme event: Phytoplankton displacement and abundance after hurricane induced flooding in the Pamlico-Albemarle Sound system,North Carolina

Airborne laser-induced fluorescence measurements were used to detect and monitor ecosystem wide changes in the distribution and concentration of chlorophyll biomass and colored dissolved organic matter in the Pamlico-Albemarle Sound system, North Carolina, U.S., following massive flooding caused by a series of three hurricanes in the late summer of 1999. These high-resolution data provided a significantly more detailed representation of the overall changes occurring in the system than could have been achieved by synoptic sampling from any other platform. The response time for the distribution of chlorophyll biomass to resume pre-flood conditions was used as a measure of ecosystem stability. Chlorophyll biomass patterns were reestablished within four mo of the flooding, whereas higher chlorophylla biomass concentrations persisted for approximately 6 mo. The primary trophic level in the Pamlico-Albemarle Sound system returned to equilibrium in less than a year of a major perturbation.